AI spend is accelerating fast enough that “we’ll clean it up later” is no longer a safe plan.
As spend ramps up, the cost model becomes harder to explain because AI usage is a chain of steps spread across services, teams, and shared platforms.
IDC warns that global 1,000 firms will underestimate AI infrastructure costs by about 30% through 2027. Underestimation happens because the bill is organized around meters, while leadership decisions are made around workflows and outcomes.
That gap shows up in how leaders describe ROI. Deloitte notes that some firms report AI consuming up to half of IT spend, cloud bills rising 19% in 2025 as generative AI workloads become central to operations.
Only 28% of finance leaders report clear, measurable value from their AI investments, highlighting the growing tension between cost growth and perceived ROI.
In this edition, I will break down why the mapping fails, and the operating patterns teams use to connect usage, cost, and outcomes without turning AI into a recurring budget reset.
Tagging won’t save you if no one owns the workflow
Teams often treat AI cost attribution the same way they treat cloud cost attribution. Improve tags. Improve allocation. Publish a better dashboard.
That is necessary, but not sufficient. AI spend grows through workflows. When a workflow has neither an owner nor a unit metric, spend is treated as “shared overhead” by default.
FinOps can show which services ran. It cannot answer the governance question that leadership cares about. Which workflows deserve more budget because they produce measurable outcomes?
You can see the difference in how cost-per-outcome works in real deployments.
Air India’s Azure-based assistant handles about 10,000 passenger queries a day, and Microsoft reports it saves several million dollars a year by deflecting contact-center interactions. What matters here is not only the automation.
Each automated query is logged and costed, then compared against the avoided cost of a call. That produces a cost-per-query metric that leadership can manage.
What to do about it
- Pick 3–5 AI workflows with real usage and name an owner accountable for both cost and outcome.
- Define one unit metric per workflow that the business recognizes (cost per query resolved, cost per document processed, cost per case closed).
- Make traceability non-negotiable. Log a workflow or request ID in the application layer so usage can be attributed beyond tags. Azure guidance explicitly points to custom logging of request identifiers or user IDs for detailed attribution.
Stay updated with Simform’s weekly insights.
Shared capacity turns spikes into ownership disputes
Even with owners and unit metrics, attribution breaks again when multiple products share the same underlying capacity. One model endpoint serves several applications.
A pooled GPU cluster runs multiple jobs. A shared retrieval layer supports multiple workflows. On the bill, the drivers collapse into a few large meters that look “central.”
This is where governance stalls in mid-market environments. Every team can justify its usage in isolation, but nobody can prove what drove the spike. The platform team can only point to aggregate consumption, which is rarely decision-grade.
The fix is to develop rules that everyone agrees to. That is also where analyst guidance is heading. Leading organizations are moving toward cost per model, per token, and per transaction, so spend can be allocated to align with outcomes.
What to do about it
- Treat your shared AI platform like an internal product with published allocation rules.
- Define what counts as shared vs owned spend (endpoints, GPU pools, retrieval stores, orchestration).
- Allocate shared costs using a driver that matches reality (requests, tokens, runtime, active users), not headcount.
- Add a threshold rule. When a workflow crosses a spend band or unit-cost band, it triggers a review before it scales further.
AI usage is variable, so small inefficiencies scale fast
Traditional cloud spend tends to scale with traffic and infrastructure footprint. AI spend scales with behavior. Prompt length grows. Retry loops creep in. Workflows fall back to larger models when the first attempt fails. Orchestration layers do more steps over time.
This is why AI cost surprises can happen without any architecture change. The workflow stayed the same on paper. The runtime behavior changed.
Azure provides the signals you need to manage this, but only if you instrument at the workflow level. Azure Monitor exposes metrics such as the number of requests and tokens processed, and you can correlate usage metrics with billing to estimate real-time cost per request.
It also highlights token spikes to alert on runaway prompts or misrouted workflows.
What to do about it
- Track tokens per workflow run and trend them as a weekly signal.
- Log retries, fallbacks, and multi-pass behavior as first-class metrics.
- Alert on unit-cost drift
- Use cost-aware defaults where Azure guidance is explicit. Batching requests and prompt caching can materially reduce cost per inference, with cached tokens billed at reduced or no cost in some cases.
AI costs include the work around inference
Most teams budget for inference because it is the obvious meter. Production AI includes the supporting work that makes AI usable day after day. Data movement. Retrieval and indexing. Orchestration. Monitoring. Evaluation. Human review paths.
These costs rarely sit in one place, which is why pilots look cheap and scaled deployments look expensive.
A useful pattern here is not “AI cost” as a single number. It is a cost map per workflow.
C.H. Robinson’s Azure AI workflow for freight quotes cut response time to 32 seconds and projected 15% productivity improvement in that process. They pass metadata through the workflow so costs can be allocated to customers or lanes and correlated with revenue.
What to do about it
- Build a cost map per high-usage workflow before usage scales.
- List the dependencies (retrieval/indexing, storage, orchestration, monitoring, review steps).
- Trace those costs back to the workflow owner, even when components are shared.
- Separate run costs from improvement costs (evaluation cycles, prompt/model iteration, re-indexing) so finance does not treat everything as a single blob.
You do not get decision-grade AI cost clarity by digging deeper into the cloud bill. You get it by connecting a workflow run to the resources it consumed and the outcome it produced.
A workable stack has five parts:
- Workflow definition with owners and unit metrics
- Traceability using workflow or request IDs
- Workflow-level cost drivers (tokens, retries, fallbacks, runtime, review steps)
- Shared-cost rules that are explicit and reviewed
- Guardrails tied to unit economics
This is the minimum structure required to decide what should scale and what needs redesign before it gets more budget. If you want to benchmark this in your Azure environment, start with our Azure Cost Optimization Assessment.
